https://github.com/VarnithChordia/Multimodal_Classification_Co_Attention
공식 코드Large Scale Multimodal Classification Using an Ensemble of Transformer Models and Co-Attention
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Varyn/MISH
공식 코드https://github.com/VegB/Text_Infilling
공식 코드https://github.com/VieVie31/visual-similarity
공식 코드Learning an Adaptation Function to Assess Image Visual Similarities
https://github.com/VisionLearningGroup/Text-to-Clip_Retrieval
공식 코드Multilevel Language and Vision Integration for Text-to-Clip Retrieval
https://github.com/Visual-Computing/GPR1200
공식 코드GPR1200: A Benchmark for General-Purpose Content-Based Image Retrieval
https://github.com/Visual-Computing/MCIP
공식 코드Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment
https://github.com/VodLM/vod
공식 코드https://github.com/WACV2025Submission/ContextIQ
공식 코드ContextIQ: A Multimodal Expert-Based Video Retrieval System for Contextual Advertising
https://github.com/WDdeBWT/CROLoss
공식 코드CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems
https://github.com/WING-NUS/scisumm-corpus
공식 코드https://github.com/WUHU-G/RCC_Transformer
공식 코드Recurrent Context Compression: Efficiently Expanding the Context Window of LLM
https://github.com/WangYuxuan93/CVLUE
공식 코드CVLUE: A New Benchmark Dataset for Chinese Vision-Language Understanding Evaluation
https://github.com/Wangjing1551/LogoDet-3K-Dataset
공식 코드https://github.com/WanzhengZhu/GPS
공식 코드Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech
https://github.com/WeixiongLin/PMC-CLIP
공식 코드PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents
https://github.com/WenjieWWJ/FoodRec
공식 코드https://github.com/WenqiJiang/SC-ANN-FPGA
공식 코드https://github.com/WenzhengZhang/hard-nce-el
공식 코드Understanding Hard Negatives in Noise Contrastive Estimation
https://github.com/WesleyZhang1991/GCN_rerank
공식 코드Graph Convolution for Re-ranking in Person Re-identification
https://github.com/William-N-Havard/VGS-dataset-metadata
공식 코드https://github.com/X-BrainLab/PI-ReID
공식 코드Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians
https://github.com/X-LANCE/SLAM-LLM
공식 코드DRCap: Decoding CLAP Latents with Retrieval-Augmented Generation for Zero-shot Audio Captioning
https://github.com/X7J92/DMR-JRG
공식 코드Dual-task Mutual Reinforcing Embedded Joint Video Paragraph Retrieval and Grounding
https://github.com/XJhaoren/IME_layer
공식 코드Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
https://github.com/XJhaoren/PWA
공식 코드Unsupervised Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval
https://github.com/XLechter/PCAN
공식 코드PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval
https://github.com/XiaoxiaoGuo/fashion-iq
공식 코드Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback
https://github.com/XiaoxiaoGuo/fashion-retrieval
공식 코드https://github.com/XiaoxiaoGuo/rcdqn
공식 코드https://github.com/XinhaoMei/audio-text_retrieval
공식 코드https://github.com/XinnuoXu/AugNLG
공식 코드AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation
https://github.com/XinshaoAmosWang/Ranked-List-Loss-for-DML
공식 코드https://github.com/XuFengthucs/frPCA_sparse
공식 코드https://github.com/Xuanmeng-Zhang/gnn-re-ranking
공식 코드Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
https://github.com/Xun-Yang/Causal_Video_Moment_Retrieval
공식 코드Deconfounded Video Moment Retrieval with Causal Intervention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.